A Dissertation Submitted to the State University of New York in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering

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Description

Radical and disruptive technological approaches regularly require experimental prototypes be built, which is difficult to justify considering their oft-prohibitive requirements in terms of financial and/or time commitments. It is also frequently the situation that use cases for new technologies are not entirely worked out precisely which in turn make it even more difficult to build prototypes but the analysis of simulation data sets from virtual samples can be used to predict sensitivity to the devised signal, detection limits, and impact of design rules and material sets. The results can thus be used to guide prototype design. The aim of this work is to develop and demonstrate a predictive approach to technology assessment and prototype
design. This work will focus on two such disruptive technology concepts: electron beam defect inspection and critical dimension measurement. These two concepts are based on the transfer from conventional process metrology technologies i.e., brightfield inspection and optical critical dimension scatterometry to multi-electron beam approaches.
Here, a multi-scale modeling approach is used to simulate data streams nominally generated by virtual tools inspecting virtual wafers. To this end, Java Monte Carlo Simulator for Secondary Electrons (JMONSEL) simulations are used to generate expected imaging responses of chosen test cases of patterns and defects with ability to vary parameters for beam
energy, spot size, pixel size, and/or defect material and form factor. The patterns are representative of the design rules for aggressively-scaled FinFET-type designs. With these simulated images and resulting shot noise, a signal-to-noise framework is developed, which relates to defect detection probabilities. Additionally, with this infrastructure the effect of detection chain noise and frequency dependent system response can be made, allowing for targeting of best recipe parameters for multi-electron beam inspection validation experiments. Ultimately, leading to insights into how such parameters will impact tool design, including necessary doses for defect detection and estimations of scanning speeds for achieving high throughput for high-volume manufacturing. Simulated images are also executed for measurement of critical dimensions of the abovementioned class of FinFETs. Similarly, validation experiments for multi-electron critical dimension measurements may use the information extracted for development of volume manufacturing metrology systems.